DBMS > Apache Kylin vs. Quasardb vs. Riak KV vs. Solr

System Properties Comparison Apache Kylin vs. Quasardb vs. Riak KV vs. Solr

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Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Distributed, high-performance timeseries database

Distributed, fault tolerant key-value store

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Search engine

Secondary database models

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.20
Rank	#175	Overall
	#80	Relational DBMS

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Score	4.44
Rank	#80	Overall
	#9	Key-value stores

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

kylin.apache.org

quasar.ai

solr.apache.org

Technical documentation

kylin.apache.org/docs

doc.quasar.ai/master

www.tiot.jp/riak-docs/riak/kv/latest

solr.apache.org/resources.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

quasardb

OpenSource, formerly Basho Technologies

Apache Software Foundation

Initial release

2015

2009

2006

Current release

3.1.0, July 2020

3.14.1, January 2024

3.2.0, December 2022

9.5.0, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache version 2, commercial enterprise edition

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Erlang

Java

Server operating systems

Linux

BSD
Linux
OS X
Windows

Linux
OS X

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

integer and binary

yes

supports customizable data types and automatic typing

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

with tags

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HTTP API

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Erlang

Java plugins

Triggers

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with selectable replication factor

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

User concepts

Access control

Cryptographically strong user authentication and audit trail

yes, using Riak Security

yes

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